Introduction to Probability and Statistics
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
Module version of WS 2021/2 (current)
There are historic module descriptions of this module. A module description is valid until replaced by a newer one.
Whether the module’s courses are offered during a specific semester is listed in the section Courses, Learning and Teaching Methods and Literature below.
|available module versions|
|WS 2021/2||SS 2021||WS 2020/1||WS 2019/20|
MA0009 is a semester module in German language at Bachelor’s level which is offered in winter semester.
This Module is included in the following catalogues within the study programs in physics.
- Further Modules from Other Disciplines
|Total workload||Contact hours||Credits (ECTS)|
|270 h||120 h||9 CP|
Content, Learning Outcome and Preconditions
- statistical models, estimators, statistical tests.
- introduction to R, transformation and visualisation of data in R, illustration of probabilistic concepts in R, implementation and practical comparison of statistical methods in R, communication of results of data analysis using R.
- understand basic models, concepts, and methods from probability theory and statistics, and formulate them in a mathematically precise way.
- discuss and prove connections between these concepts and illustrate them with examples; solve problems using these concepts and the methods covered in the class
- model simple random experiments and statistical methods and implement them in a computer program
- interpret statistical data and methods, visualize data, and assess the meaning of random experiments
MA0002 - Analysis 2
MA0004 - Linear Algebra 1
MA0005 - Linear Algebra 2 und Discrete Structures
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
Please keep in mind that course announcements are regularly only completed in the semester before.
Learning and Teaching Methods
After this was done in the beginnung under guidance during the exercises sessions and the practical supplement, this is deepened during the semester individually and partially also in small groups.
Kersting, G., Wakolbinger, A. (2008). Elementare Stochastik. Birkhäuser, Basel.
Wickham, H. und Grolemund, G. (2017). R for Data Science. O'Reilly.
Grimmett, G., Stirzaker, D. (2001). Probability and Random Processes. Third Edition. Oxford University Press, Oxford.
Dehling, H., Haupt, B. (2004). Einführung in die Wahrscheinlichkeitstheorie und Statistik. 2. Auflage. Springer, Berlin.
Description of exams and course work
- can formulate basic models and concepts from probability and statistics in a mathematically precise way and use them accurately,
- can model simple random experiments and statistical problems, understand R programs and interpret their output,
- can interpret statistical data and evaluate the outcome of random experiments.
The exam may be repeated at the end of the semester.